Overview

Dataset statistics

Number of variables8
Number of observations2429
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory154.2 KiB
Average record size in memory65.0 B

Variable types

DateTime1
TimeSeries5
Boolean1
Numeric1

Timeseries statistics

Number of series5
Time series length2429
Starting point2010-01-04 00:00:00
Ending point2019-08-30 00:00:00
Period1 day, 10 hours and 50 minutes
2026-02-06T03:48:24.096465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:24.297001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

repaired? has constant value "False"Constant
adj close is highly overall correlated with close and 3 other fieldsHigh correlation
close is highly overall correlated with adj close and 3 other fieldsHigh correlation
high is highly overall correlated with adj close and 3 other fieldsHigh correlation
low is highly overall correlated with adj close and 3 other fieldsHigh correlation
open is highly overall correlated with adj close and 3 other fieldsHigh correlation
adj close is non stationaryNon stationary
close is non stationaryNon stationary
high is non stationaryNon stationary
low is non stationaryNon stationary
open is non stationaryNon stationary
adj close is seasonalSeasonal
close is seasonalSeasonal
high is seasonalSeasonal
low is seasonalSeasonal
open is seasonalSeasonal
Date has unique valuesUnique
volume has 32 (1.3%) zerosZeros

Reproduction

Analysis started2026-02-06 03:48:19.707424
Analysis finished2026-02-06 03:48:23.996199
Duration4.29 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Date
Date

Unique 

Distinct2429
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Minimum2010-01-04 00:00:00
Maximum2019-08-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-06T03:48:24.463571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:24.582932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

adj close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1869
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.9911
Minimum1050.8
Maximum1888.7
Zeros0
Zeros (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:24.729090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1050.8
5-th percentile1117.52
Q11222.6
median1289.4
Q31405.6
95-th percentile1719.72
Maximum1888.7
Range837.8999
Interquartile range (IQR)183

Descriptive statistics

Standard deviation180.12807
Coefficient of variation (CV)0.1342245
Kurtosis0.048971631
Mean1341.9911
Median Absolute Deviation (MAD)79.599976
Skewness0.98295842
Sum3259696.3
Variance32446.123
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3241636222
2026-02-06T03:48:24.862605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-06T03:48:25.219181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-06T03:48:26.533223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.6999516
 
0.2%
1313.6999515
 
0.2%
1225.3000494
 
0.2%
13194
 
0.2%
1251.6999514
 
0.2%
1241.0999764
 
0.2%
1254.3000494
 
0.2%
1273.4000244
 
0.2%
1291.5999764
 
0.2%
1293.3000494
 
0.2%
Other values (1859)2386
98.2%
ValueCountFrequency (%)
1050.8000491
< 0.1%
1052.1999511
< 0.1%
1054.1999511
< 0.1%
1056.1999511
< 0.1%
1060.0999761
< 0.1%
1060.3000491
< 0.1%
1061.6999511
< 0.1%
1062.4000241
< 0.1%
1062.9000241
< 0.1%
1063.8000491
< 0.1%
ValueCountFrequency (%)
1888.6999511
< 0.1%
1873.6999511
< 0.1%
1869.9000241
< 0.1%
1858.3000491
< 0.1%
1856.4000241
< 0.1%
1854.4000241
< 0.1%
1848.9000241
< 0.1%
1828.51
< 0.1%
1826.8000491
< 0.1%
1826.6999511
< 0.1%
2026-02-06T03:48:24.984790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1869
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.9911
Minimum1050.8
Maximum1888.7
Zeros0
Zeros (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:27.294597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1050.8
5-th percentile1117.52
Q11222.6
median1289.4
Q31405.6
95-th percentile1719.72
Maximum1888.7
Range837.8999
Interquartile range (IQR)183

Descriptive statistics

Standard deviation180.12807
Coefficient of variation (CV)0.1342245
Kurtosis0.048971631
Mean1341.9911
Median Absolute Deviation (MAD)79.599976
Skewness0.98295842
Sum3259696.3
Variance32446.123
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3241636222
2026-02-06T03:48:27.428717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-06T03:48:27.785790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-06T03:48:28.782123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.6999516
 
0.2%
1313.6999515
 
0.2%
1225.3000494
 
0.2%
13194
 
0.2%
1251.6999514
 
0.2%
1241.0999764
 
0.2%
1254.3000494
 
0.2%
1273.4000244
 
0.2%
1291.5999764
 
0.2%
1293.3000494
 
0.2%
Other values (1859)2386
98.2%
ValueCountFrequency (%)
1050.8000491
< 0.1%
1052.1999511
< 0.1%
1054.1999511
< 0.1%
1056.1999511
< 0.1%
1060.0999761
< 0.1%
1060.3000491
< 0.1%
1061.6999511
< 0.1%
1062.4000241
< 0.1%
1062.9000241
< 0.1%
1063.8000491
< 0.1%
ValueCountFrequency (%)
1888.6999511
< 0.1%
1873.6999511
< 0.1%
1869.9000241
< 0.1%
1858.3000491
< 0.1%
1856.4000241
< 0.1%
1854.4000241
< 0.1%
1848.9000241
< 0.1%
1828.51
< 0.1%
1826.8000491
< 0.1%
1826.6999511
< 0.1%
2026-02-06T03:48:27.551741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1877
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1348.795
Minimum1062
Maximum1911.6
Zeros0
Zeros (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:29.508412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1062
5-th percentile1124.12
Q11226.6
median1293.8
Q31413.9
95-th percentile1728.26
Maximum1911.6
Range849.59998
Interquartile range (IQR)187.30005

Descriptive statistics

Standard deviation181.89468
Coefficient of variation (CV)0.13485717
Kurtosis0.073503016
Mean1348.795
Median Absolute Deviation (MAD)79
Skewness0.99474273
Sum3276223.1
Variance33085.673
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3242207128
2026-02-06T03:48:29.641209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-06T03:48:29.992086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-06T03:48:31.297395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1320.6999515
 
0.2%
12365
 
0.2%
1235.1999514
 
0.2%
1273.54
 
0.2%
1254.54
 
0.2%
1291.5999764
 
0.2%
12924
 
0.2%
1292.6999514
 
0.2%
12344
 
0.2%
13124
 
0.2%
Other values (1867)2387
98.3%
ValueCountFrequency (%)
10621
< 0.1%
1064.5999761
< 0.1%
1066.1999511
< 0.1%
1068.4000241
< 0.1%
1068.51
< 0.1%
1069.51
< 0.1%
1070.1999511
< 0.1%
1070.3000491
< 0.1%
1071.51
< 0.1%
1071.9000241
< 0.1%
ValueCountFrequency (%)
1911.5999761
< 0.1%
1909.3000491
< 0.1%
18951
< 0.1%
1884.1999511
< 0.1%
1881.3000491
< 0.1%
1874.4000241
< 0.1%
1873.6999511
< 0.1%
18701
< 0.1%
1853.0999761
< 0.1%
1852.4000241
< 0.1%
2026-02-06T03:48:29.758195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

low
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1909
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1334.8976
Minimum1045.2
Maximum1864
Zeros0
Zeros (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:32.023755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1045.2
5-th percentile1110
Q11216.2
median1284.3
Q31395.1
95-th percentile1708.7
Maximum1864
Range818.80005
Interquartile range (IQR)178.90002

Descriptive statistics

Standard deviation178.30699
Coefficient of variation (CV)0.13357353
Kurtosis0.029777162
Mean1334.8976
Median Absolute Deviation (MAD)78.900024
Skewness0.97132185
Sum3242466.2
Variance31793.381
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3214246219
2026-02-06T03:48:32.156813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-06T03:48:32.511757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-06T03:48:33.522578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1244.8000495
 
0.2%
1222.1999515
 
0.2%
1218.5999765
 
0.2%
1328.54
 
0.2%
12404
 
0.2%
1279.4000244
 
0.2%
12514
 
0.2%
1287.54
 
0.2%
1224.54
 
0.2%
11914
 
0.2%
Other values (1899)2386
98.2%
ValueCountFrequency (%)
1045.1999511
< 0.1%
1046.1999511
< 0.1%
1049.5999761
< 0.1%
1050.51
< 0.1%
1051.0999761
< 0.1%
1052.0999761
< 0.1%
1052.6999511
< 0.1%
1058.51
< 0.1%
1058.6999511
< 0.1%
10591
< 0.1%
ValueCountFrequency (%)
18641
< 0.1%
1858.4000241
< 0.1%
18351
< 0.1%
18301
< 0.1%
1828.5999761
< 0.1%
1824.5999761
< 0.1%
1823.6999511
< 0.1%
1814.4000241
< 0.1%
1811.4000241
< 0.1%
18091
< 0.1%
2026-02-06T03:48:32.278037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1883
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.2029
Minimum1052.2
Maximum1909
Zeros0
Zeros (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:34.305630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1052.2
5-th percentile1118.04
Q11222.9
median1289.4
Q31406.4
95-th percentile1720.96
Maximum1909
Range856.80005
Interquartile range (IQR)183.5

Descriptive statistics

Standard deviation180.3753
Coefficient of variation (CV)0.13438751
Kurtosis0.055593132
Mean1342.2029
Median Absolute Deviation (MAD)79.099976
Skewness0.98621484
Sum3260210.8
Variance32535.249
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3328374599
2026-02-06T03:48:34.435667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-06T03:48:35.110512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-06T03:48:36.143741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.55
 
0.2%
1252.6999515
 
0.2%
1292.0999765
 
0.2%
1279.4000245
 
0.2%
1239.3000494
 
0.2%
1224.8000494
 
0.2%
1199.6999514
 
0.2%
1268.4000244
 
0.2%
1612.8000494
 
0.2%
1366.8000494
 
0.2%
Other values (1873)2385
98.2%
ValueCountFrequency (%)
1052.1999511
< 0.1%
1053.6999511
< 0.1%
1054.4000241
< 0.1%
1056.51
< 0.1%
1061.9000241
< 0.1%
10631
< 0.1%
1063.4000241
< 0.1%
10641
< 0.1%
1064.5999762
0.1%
1064.8000491
< 0.1%
ValueCountFrequency (%)
19091
< 0.1%
1886.3000491
< 0.1%
1868.9000241
< 0.1%
1868.5999761
< 0.1%
1858.0999761
< 0.1%
1852.4000241
< 0.1%
18431
< 0.1%
1839.3000491
< 0.1%
18331
< 0.1%
1830.5999761
< 0.1%
2026-02-06T03:48:34.555706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

repaired?
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
False
2429 
ValueCountFrequency (%)
False2429
100.0%
2026-02-06T03:48:36.847604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

volume
Real number (ℝ)

Zeros 

Distinct906
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5816.2812
Minimum0
Maximum386334
Zeros32
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2026-02-06T03:48:36.928225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q135
median126
Q3426
95-th percentile6975.4
Maximum386334
Range386334
Interquartile range (IQR)391

Descriptive statistics

Standard deviation30563.602
Coefficient of variation (CV)5.2548357
Kurtosis48.621025
Mean5816.2812
Median Absolute Deviation (MAD)111
Skewness6.6991102
Sum14127747
Variance9.3413375 × 108
MonotonicityNot monotonic
2026-02-06T03:48:37.061634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
1.3%
2025
 
1.0%
1424
 
1.0%
723
 
0.9%
523
 
0.9%
423
 
0.9%
823
 
0.9%
2722
 
0.9%
222
 
0.9%
121
 
0.9%
Other values (896)2191
90.2%
ValueCountFrequency (%)
032
1.3%
121
0.9%
222
0.9%
318
0.7%
423
0.9%
523
0.9%
617
0.7%
723
0.9%
823
0.9%
917
0.7%
ValueCountFrequency (%)
3863341
< 0.1%
2908891
< 0.1%
2805461
< 0.1%
2761361
< 0.1%
2754421
< 0.1%
2714571
< 0.1%
2590501
< 0.1%
2544281
< 0.1%
2471681
< 0.1%
2374971
< 0.1%

Interactions

2026-02-06T03:48:23.309209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.106793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.568838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.000585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.435800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.871807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.396737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.183102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.639523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.071668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.505736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.941592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.482279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.260826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.707523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.140546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.574825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.013072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.569657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.337523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.776807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.211012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.645836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.083492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.653896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.413908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.847218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.280747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.714892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.152907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.740868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.484400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:21.916206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.349570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:22.786225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T03:48:23.223902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-06T03:48:37.155225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adj closeclosehighlowopenvolume
adj close1.0001.0000.9980.9980.9960.016
close1.0001.0000.9980.9980.9960.016
high0.9980.9981.0000.9960.9980.028
low0.9980.9980.9961.0000.9980.002
open0.9960.9960.9980.9981.0000.017
volume0.0160.0160.0280.0020.0171.000

Missing values

2026-02-06T03:48:23.872426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-06T03:48:23.951892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Dateadj closeclosehighlowopenrepaired?volume
2010-01-042010-01-041117.6999511117.6999511122.3000491097.0999761117.699951False184
2010-01-052010-01-051118.0999761118.0999761126.5000001115.0000001118.099976False53
2010-01-062010-01-061135.9000241135.9000241139.1999511120.6999511135.900024False363
2010-01-072010-01-071133.0999761133.0999761133.0999761129.1999511133.099976False56
2010-01-082010-01-081138.1999511138.1999511138.1999511122.6999511138.199951False54
2010-01-112010-01-111150.6999511150.6999511161.1999511143.0000001150.699951False177
2010-01-122010-01-121128.9000241128.9000241157.1999511127.1999511128.900024False51
2010-01-132010-01-131136.4000241136.4000241136.4000241121.0000001136.400024False58
2010-01-142010-01-141142.5999761142.5999761145.9000241132.8000491137.000000False81
2010-01-152010-01-151130.0999761130.0999761133.4000241127.1999511132.800049False50
Dateadj closeclosehighlowopenrepaired?volume
2019-08-192019-08-191500.4000241500.4000241507.5999761492.9000241507.199951False205
2019-08-202019-08-201504.5999761504.5999761506.0999761497.5000001497.500000False486
2019-08-212019-08-211504.5999761504.5999761505.0000001498.8000491504.900024False350
2019-08-222019-08-221497.3000491497.3000491497.3000491493.8000491495.099976False686
2019-08-232019-08-231526.5999761526.5999761527.5999761493.5000001493.500000False983
2019-08-262019-08-261526.3000491526.3000491543.3000491524.3000491543.199951False334
2019-08-272019-08-271541.0000001541.0000001542.0999761528.5000001530.500000False166
2019-08-282019-08-281537.8000491537.8000491538.0999761537.8000491538.099976False2756
2019-08-292019-08-291526.5000001526.5000001549.3000491519.5999761537.500000False704
2019-08-302019-08-301519.0999761519.0999761530.4000241516.6999511524.599976False276